The Test of Herding Behavior in the Shanghai-Shenzhen Stock Market When the Price Undulates Violently

2006 ◽  
Author(s):  
Yan-chun Liu ◽  
Jing Guan
Keyword(s):  
Stock Market ◽  
Herding Behavior

Portfolio Performance in Relation to Herding Behavior in the Taiwan Stock Market

2012 ◽  
Vol 48 (sup2) ◽  
pp. 82-104 ◽  
Author(s):  
Chiao-Yi Chang ◽  
Hsiang-Lan Chen ◽  
Zong-Ru Jiang
Keyword(s):  
Stock Market ◽  
Portfolio Performance ◽  
Herding Behavior

Why investors use technical analysis? Information discovery versus herding behavior

2015 ◽  
Vol 5 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Tiandu Wang ◽  
Qian Sun
Keyword(s):  
Stock Market ◽  
Information Asymmetry ◽  
Excess Return ◽  
Chinese Stock Market ◽  
Information Discovery ◽  
Herding Behavior ◽  
Significant Excess ◽  
Technical Trading Rules ◽  
Content Type ◽  
Empirical Results

Purpose – The purpose of this paper is to establish two competitive models to explain why investors use technical analysis (TA). Design/methodology/approach – Information Discovery Model suggests that technical traders are able to infer non-public information; Herding Behavior Model argues that TA is a kind of irrational herding behavior that can make profit when other noise traders exist. Findings – The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns. Research limitations/implications – The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns. Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient. Originality/value – Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient.

scholarly journals Investor Sentiment and Herding Behavior in the Korean Stock Market

2020 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Ki-Hong Choi ◽  
Seong-Min Yoon
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Markets ◽  
Trading Volume ◽  
Investor Sentiment ◽  
Herding Behavior ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Korean Stock Market ◽  
Quantile Regression Method

This paper investigates herding behavior and the connection between herding behavior and investor sentiment. We apply a Cross-Sectional Absolute Deviation (CSAD) approach and the quantile regression method to capture herding behavior in the KOSPI and KOSDAQ stock markets. The analysis results are outlined as follows. First, we find that herding behavior is exhibited during down-market periods in the KOSPI and KOSDAQ stock markets. However, we show that adverse herding behavior occurs in low-trading volume and low-volatility periods. Second, according to the results of the quantile regression, herding behavior is found in the low and high quantiles of the KOSPI and KOSDAQ stock markets. However, adverse herding behavior is also found, which means that investors herd in extreme market conditions. Third, the relationship between investor sentiment and herding behavior is analyzed through regression and quantile regression, and investor sentiment is confirmed to be one of the important factors that can cause herding behavior in the Korean stock market.

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